머신 러닝 관리자

근무 일정

• Machine Learning Managers work full-time jobs, typically with nights, weekends, and holidays off, though overtime may be occasionally needed.

일반적인 의무

• Look for areas where machine learning (ML) can be applied to existing projects and processes
• Meet with company leadership and teams to explain concepts, propose strategies, and review potential impacts and benefits
• Create a machine learning roadmap listing processes and problems, as well as the math, resources, and tools to be used
• Implement ML initiatives according to the schedule 
• Lead ML teams which may include data scientists, engineers, and programmers
• Boost user awareness of how ML is being adopted and what changes they may need to know about
• Work with mobile device management teams as needed to ensure new data strategies are implemented efficiently
• Generate and deploy algorithms capable of extracting useful information from large data sets
• Objectively assess different methodologies and their results
• Use programming languages and tools like Python, R, and TensorFlow
• Develop automated processes for predictive model validation

추가 책임

• Work with partner businesses as directed to share knowledge, insights, or information about changes
• Build strong external partnership networks to enhance learning
• Train or mentor team members and assistant managers

소프트 스킬

• 분석
• 비즈니스 통찰력
• 의사소통 기술
• 결정적인
• 디테일 지향
• 윤리
• 독립의 
• 리더십 기술
• 목표
• 조직
• 환자 
• 문제 해결
• 팀워크

기술 능력

• 인공 지능
• 빅 데이터 
• 데이터 마이닝
• 데이터 과학
• Hadoop
• 머신 러닝
• Predictive modeling
• 파이썬
• Software development life cycle
• .SQL
• 통계 분석 
• Technical domains and environments

• 컨설팅 회사
• E-commerce/retail stores
• Financial sector
• 정부 기관
• Healthcare and pharmaceutical companies
• 제조
• 연구 기관
• Tech companies

Machine Learning Managers are expected to be at the peak of their game, and ready to effectively lead teams to meet ML-related organizational goals. 

They must be creative, ethical, and forward-thinking, able to find and exploit all opportunities to integrate and leverage ML capabilities and boost performance. In this era of high-tech competitiveness, companies that fail to stay on top of trends may quickly fall behind and lose customers.

Machine learning is evolving rapidly and there are several notable trends to keep track of. Among them is the advancement of deep learning and deep neural networks inspired by the interconnected network of neurons in the human brain. Reinforcement learning is also a hot trend in robotics, training programs (aka agents) to interact with environments via trial and error. 

As ML models become more complex, researchers must pay attention to ethical considerations and how ML models make decisions. Other trends include concepts like federated learning, transfer learning and pre-trained models, AutoML, edge computing, and on-device ML—each of which Machine Learning Managers need to learn about to stay up-to-date! 

Machine Learning Managers were probably in love with technology at an early age. They may have been interested in math, computer coding, and programming languages. They likely also enjoyed analytical problem-solving or even reading about the impacts of technology on businesses. 

Teamwork is an important part of this career field, but Machine Learning Managers are leaders who must be willing to act when there is disagreement. It’s their job to ensure appropriate ML behavior and decision-making. This ability to lead could have developed through extracurricular activities at school.